Natural Gas Market Hubs and Interstate Pipeline Corridors
Contents
Natural Gas Market Hubs and Interstate Pipeline Corridors¶
Data Import¶
import pandas as pd
import matplotlib.pyplot as plt
import geopandas as gpd
import folium
import contextily as cx
import rtree
from zlib import crc32
import hashlib
from shapely.geometry import Point, LineString, Polygon
/Users/jnapolitano/venvs/finance/lib/python3.9/site-packages/geopandas/_compat.py:111: UserWarning: The Shapely GEOS version (3.10.2-CAPI-1.16.0) is incompatible with the GEOS version PyGEOS was compiled with (3.10.1-CAPI-1.16.0). Conversions between both will be slow.
warnings.warn(
Natural Gas Pipeline Data¶
## Importing our DataFrames
#gisfilepath = "/Users/jnapolitano/Projects/data/energy/Natural_Gas_Pipelines.geojson"
gisfilepath = '/Users/jnapolitano/Projects/data/energy/Natural_Gas_Liquid_Pipelines.zip'
ng_pipeline_df = gpd.read_file(gisfilepath)
ng_pipeline_df = ng_pipeline_df.to_crs(epsg=3857)
#colums = ng_pipeline_df.columns
#uniqe = ng_market_df.TYPE.unique()
ng_pipeline_df = ng_pipeline_df[ng_pipeline_df.TYPEPIPE == 'Interstate']
ng_pipeline_df.dropna(inplace=True)
ng_pipeline_df.head()
| TYPEPIPE | Operator | Shape_Leng | geometry | |
|---|---|---|---|---|
| 737 | Interstate | Enbridge Pipelines (AlaTenn) | 0.171766 | LINESTRING (-9656362.029 4119699.311, -9653795... |
| 738 | Interstate | Enbridge Pipelines (AlaTenn) | 0.174189 | LINESTRING (-9675726.277 4118469.233, -9673070... |
| 739 | Interstate | Enbridge Pipelines (AlaTenn) | 0.175478 | LINESTRING (-9675726.277 4118469.233, -9683221... |
| 740 | Interstate | Enbridge Pipelines (AlaTenn) | 0.057599 | LINESTRING (-9682131.156 4118105.433, -9675726... |
| 741 | Interstate | Enbridge Pipelines (AlaTenn) | 0.099992 | LINESTRING (-9692746.582 4114035.142, -9690740... |
Natural Gas By Operator Map Data¶
ng_pipeline_map =ng_pipeline_df.explore(
#column="Operator", # make choropleth based on "PORT_NAME" column
popup=False, # show all values in popup (on click)
tiles='Stamen Terrain',
#tiles="CartoDB positron", # use "CartoDB positron" tiles
#cmap='Reds', # use "Set1" matplotlib colormap
#m=ng_pipeline_map,
#style_kwds=dict(color="black"),
#marker_kwds= dict(radius=2),
#tooltip=['','State','Hub_name','Operator','Maxthru','Avgdaily','Numcust','Platform'],
#legend =False, # use black outline)
#categorical=True,
color='grey'
)
#ng_pipeline_map
Natural Gas Market Hub Data¶
## Importing our DataFrames
gisfilepath = "/Users/jnapolitano/Projects/data/energy/Natural_Gas_Market_Hubs.geojson"
ng_market_df = gpd.read_file(gisfilepath)
ng_market_df = ng_market_df.to_crs(epsg=3857)
ng_market_df.describe()
| FID | OBJECTID | Yr_activat | Maxthru | Avgdaily | Numcust | Yearofdata | Latitude | Longitude | |
|---|---|---|---|---|---|---|---|---|---|
| count | 27.000000 | 27.000000 | 27.000000 | 27.000000 | 27.000000 | 27.000000 | 27.000000 | 27.000000 | 27.000000 |
| mean | 14.000000 | 14.000000 | 1995.333333 | 1409.111111 | 951.481481 | 74.777778 | 2007.888889 | 36.538033 | -100.874230 |
| std | 7.937254 | 7.937254 | 4.047791 | 959.288492 | 769.353893 | 133.527391 | 1.012739 | 6.368855 | 13.339038 |
| min | 1.000000 | 1.000000 | 1988.000000 | 0.000000 | 0.000000 | 0.000000 | 2003.000000 | 27.548800 | -122.220000 |
| 25% | 7.500000 | 7.500000 | 1994.000000 | 615.000000 | 320.000000 | 10.000000 | 2008.000000 | 30.213150 | -109.284500 |
| 50% | 14.000000 | 14.000000 | 1994.000000 | 1200.000000 | 600.000000 | 40.000000 | 2008.000000 | 36.686400 | -97.600300 |
| 75% | 20.500000 | 20.500000 | 1996.000000 | 2112.500000 | 1525.000000 | 61.000000 | 2008.000000 | 41.478950 | -92.024700 |
| max | 27.000000 | 27.000000 | 2008.000000 | 3100.000000 | 2500.000000 | 660.000000 | 2009.000000 | 49.000500 | -74.178500 |
Pipelines and Market Hubs Map¶
ng_market_map =ng_market_df.explore(
column="Hub_name", # make choropleth based on "PORT_NAME" column
popup=False, # show all values in popup (on click)
tiles="Stamen Terrain", # use "CartoDB positron" tiles
cmap='Reds', # use "Set1" matplotlib colormap
m=ng_pipeline_map,
#style_kwds=dict(color="black"),
marker_kwds= dict(radius=15),
tooltip=['Region','State','Hub_name','Operator','Maxthru','Avgdaily','Numcust','Platform'],
legend =True, # use black outline)
categorical=True,
)
ng_market_map
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